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Preprint · Research Square · January 1, 2026

CAM-Threshold-Guided Weakly Supervised Lesion Segmentation and Relative Lesion-Burden Stratification of Wheat Stripe Rust Using CF2- WRD and Light-UNet

Bojian Guo, Xin Li, Nazgul Ismanovna Temirbaeva, A.Dzh. Kartanova

Asel Kartanova
Open via DOIPDF

Abstract

Abstract Accurate localization and quantification of wheat stripe rust lesions are important for phenotyping and disease management, yet dense pixel-level annotation is costly. We present a Grad-CAM-guided weakly supervised framework that reuses a dual-branch image classifier, CF2-WRD, to generate pseudo-labels for a compact Light-UNet segmenter. The dataset contained 34,694 images in 87 image-level categories, split into 27,834 training, 3,430 validation, and 3,430 test images. CF2-WRD achieved validation and test accuracies of 0.8776 and 0.8711. Among four CAM thresholds, 0.40 produced the highest agreement with CAM-derived validation pseudo-labels (mIoU 0.6584, Dice 0.7101, lesion IoU 0.5555), whereas 0.45 was used as a conservative operating point. Raising the segmentation probability threshold from 0.50 to 0.80 reduced mean predicted lesion-to-leaf ratio from 55.02% to 29.63% in a 300-image subset. The 3,430-image burden distribution was stratified by empirical quartiles. Because no independent manual lesion masks or expert-calibrated severity labels were available, overlap metrics quantify agreement with weak-supervision targets and burden groups represent dataset-relative rankings rather than agronomic severity grades.

Smart Agriculture and AIAdvanced Neural Network ApplicationsWheat and Barley Genetics and Pathologywheat stripe rustlesion segmentationweakly supervised segmentation

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